Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study.

Background: Recently, we developed and tested an autonomous artificial intelligence (AI) agent for prescribing a drug to prevent severe acute graft-versus-host disease in patients receiving human leukocyte antigen haplotype-mismatched hematopoietic cell transplants in a prospective clinical trial. Our experience in this proof-of-concept study suggests that physicians and patients can be receptive to autonomous AI prescription. However, the generalizability of our conclusion requires testing in additional clinical settings. Before broadening the scope of study of AI-driven drug prescriptions, it is important to quantify the factors that influence a physician's receptiveness to AI prescription.

Objective: We aim to systematically interrogate physicians' receptiveness to AI prescription in China.

Methods: We have designed a research protocol to survey a diverse range of factors that may affect physicians' receptiveness to AI prescription systems, including the physicians' personal attributes and their perceptions of the importance of various technological, institutional, and governmental attributes. The survey will be conducted in 2 phases. In phase 1, the survey will be limited to the Tianjin metropolitan area, enlisting >250 physicians from approximately 2 tier-1, 3 tier-2, and 3 tier-3 hospitals. In phase 2, we will survey metropolitan areas in ≥10 additional province-level administrative divisions, enlisting >1250 additional physicians from >15 tier-1, >15 tier-2, and >15 tier-3 hospitals. We hypothesize that physicians can be broadly classified into distinct psychological profile types, and furthermore, that these types are plausibly mediated by the locales where the physicians are employed and the physicians' demographics, educational and job experience, clinical subspecialties, and previous knowledge of and experience with AI. Clustering methods, including t-distributed stochastic neighbor embedding and hierarchical clustering, will be performed on respondent data to identify the distinct psychological profile types of the physicians. Multiple-variable regression and mediation analyses will be conducted to identify potential underlying mechanisms mediating physicians' receptiveness to AI prescription.

Results: At the time of submission of the manuscript, no subjects have been recruited. The survey study was approved by the institutional ethics committee and funded in May 2025, and we started recruiting respondents in May 2025. We plan to complete phase 1 by September 30, 2025, and phase 2 by November 30, 2025. Anonymized survey results and their analyses are expected to be published in a peer-reviewed journal in fall 2026.

Conclusions: We anticipate that data and analytical insights generated from this study will assist policy makers and AI researchers in prioritizing a data-informed sequence of developing and promoting AI prescription tools in successive regions, disciplines, and clinical use cases and inform policy makers to match resource allocation with "AI readiness."

International Registered Report Identifier (irrid): PRR1-10.2196/76009.